I have a large dataframe (t2m.all) with longitude (lon), latitude (lat) and hourly temperatures since 1958 (X1958.01.01.00.00.00). A subset of the data is given in the code below:
dput(t2m.all[1:300,1:3])
structure(list(Lon = c(-102, -101.9, -101.8, -101.7, -101.6,
-101.5, -101.4, -101.3, -101.2, -101.1, -101, -100.9, -100.8,
-100.7, -100.6, -100.5, -100.4, -100.3, -100.2, -100.1, -100,
-99.9, -99.8, -99.7, -99.6, -99.5, -99.4, -99.3, -99.2, -99.1,
-99, -98.9, -98.8, -98.7, -98.6, -98.5, -98.4, -98.3, -98.2,
-98.1, -98, -97.9, -97.8, -97.7, -97.6, -97.5, -97.4, -97.3,
-97.2, -97.1, -97, -96.9, -96.8, -96.7, -96.6, -96.5, -96.4,
-96.3, -96.2, -96.1, -96, -95.9, -95.8, -95.7, -95.6, -95.5,
-95.4, -95.3, -95.2, -95.1, -95, -102, -101.9, -101.8, -101.7,
-101.6, -101.5, -101.4, -101.3, -101.2, -101.1, -101, -100.9,
-100.8, -100.7, -100.6, -100.5, -100.4, -100.3, -100.2, -100.1,
-100, -99.9, -99.8, -99.7, -99.6, -99.5, -99.4, -99.3, -99.2,
-99.1, -99, -98.9, -98.8, -98.7, -98.6, -98.5, -98.4, -98.3,
-98.2, -98.1, -98, -97.9, -97.8, -97.7, -97.6, -97.5, -97.4,
-97.3, -97.2, -97.1, -97, -96.9, -96.8, -96.7, -96.6, -96.5,
-96.4, -96.3, -96.2, -96.1, -96, -95.9, -95.8, -95.7, -95.6,
-95.5, -95.4, -95.3, -95.2, -95.1, -95, -102, -101.9, -101.8,
-101.7, -101.6, -101.5, -101.4, -101.3, -101.2, -101.1, -101,
-100.9, -100.8, -100.7, -100.6, -100.5, -100.4, -100.3, -100.2,
-100.1, -100, -99.9, -99.8, -99.7, -99.6, -99.5, -99.4, -99.3,
-99.2, -99.1, -99, -98.9, -98.8, -98.7, -98.6, -98.5, -98.4,
-98.3, -98.2, -98.1, -98, -97.9, -97.8, -97.7, -97.6, -97.5,
-97.4, -97.3, -97.2, -97.1, -97, -96.9, -96.8, -96.7, -96.6,
-96.5, -96.4, -96.3, -96.2, -96.1, -96, -95.9, -95.8, -95.7,
-95.6, -95.5, -95.4, -95.3, -95.2, -95.1, -95, -94.9, -102, -101.9,
-101.8, -101.7, -101.6, -101.5, -101.4, -101.3, -101.2, -101.1,
-101, -100.9, -100.8, -100.7, -100.6, -100.5, -100.4, -100.3,
-100.2, -100.1, -100, -99.9, -99.8, -99.7, -99.6, -99.5, -99.4,
-99.3, -99.2, -99.1, -99, -98.9, -98.8, -98.7, -98.6, -98.5,
-98.4, -98.3, -98.2, -98.1, -98, -97.9, -97.8, -97.7, -97.6,
-97.5, -97.4, -97.3, -97.2, -97.1, -97, -96.9, -96.8, -96.7,
-96.6, -96.5, -96.4, -96.3, -96.2, -96.1, -96, -95.9, -95.8,
-95.7, -95.6, -95.5, -95.4, -95.3, -95.2, -95.1, -95, -94.9,
-102, -101.9, -101.8, -101.7, -101.6, -101.5, -101.4, -101.3,
-101.2, -101.1, -101, -100.9, -100.8, -100.7), Lat = c(60, 60,
60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60,
60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60,
60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60,
60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60,
60, 60, 60, 60, 60, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9,
59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9,
59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9,
59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9,
59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9,
59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9,
59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.9, 59.8, 59.8,
59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8,
59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8,
59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8,
59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8,
59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8,
59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8, 59.8,
59.8, 59.8, 59.8, 59.8, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7,
59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7,
59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7,
59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7,
59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7,
59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7,
59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.7, 59.6,
59.6, 59.6, 59.6, 59.6, 59.6, 59.6, 59.6, 59.6, 59.6, 59.6, 59.6,
59.6, 59.6), X1958.01.01.00.00.00 = c(-37.2, -37.2, -36.6, -35.9,
-36, -36.1, -35.8, -35.5, -35.1, -34.7, -34.2, -33.6, -33.6,
-34, -33.8, -33.2, -32.8, -32.6, -32.5, -32.5, -32.4, -32.3,
-32.3, -32.7, -33.1, -33.2, -33.3, -33.3, -33.3, -33.1, -32.9,
-32.6, -32.4, -32.1, -31.8, -31.5, -31.2, -30.9, -30.6, -30.3,
-30, -29.7, -29.4, -29.1, -28.8, -28.5, -28.2, -27.9, -27.7,
-27.4, -27.1, -26.8, -26.6, -26.3, -26.1, -25.9, -25.6, -25.4,
-25.2, -24.9, -24.7, -24.4, -24.1, -23.8, -23.6, -23.3, -23,
-22.7, -22.4, -22.1, -21.9, -36.6, -37.1, -36.6, -36.1, -36,
-36.1, -35.7, -35, -34.6, -34.8, -34.7, -34, -33.8, -33.7, -33.6,
-33.2, -32.4, -32.3, -32.3, -32.3, -32.3, -32.8, -32.9, -32.7,
-33, -33.1, -33.1, -33.1, -33.1, -33, -32.8, -32.5, -32.3, -32.1,
-31.8, -31.5, -31.2, -30.9, -30.6, -30.3, -30.1, -29.8, -29.4,
-29.1, -28.8, -28.5, -28.3, -28, -27.7, -27.4, -27.1, -26.9,
-26.6, -26.4, -26.1, -25.9, -25.6, -25.4, -25.2, -24.9, -24.7,
-24.4, -24.1, -23.9, -23.6, -23.3, -23, -22.8, -22.5, -22.2,
-21.9, -36.7, -36.8, -36.8, -36.4, -36.2, -36, -35.9, -35.2,
-34.6, -34.6, -34.4, -34.2, -34.2, -33.8, -33.3, -32.7, -32.3,
-32.2, -32.3, -32.6, -33.3, -33.5, -33.2, -33.1, -32.5, -32.2,
-32.7, -33, -32.9, -32.8, -32.6, -32.5, -32.2, -32, -31.7, -31.5,
-31.2, -30.9, -30.6, -30.4, -30.1, -29.8, -29.5, -29.2, -28.9,
-28.6, -28.3, -28, -27.7, -27.4, -27.2, -26.9, -26.7, -26.4,
-26.2, -25.9, -25.7, -25.4, -25.2, -24.9, -24.7, -24.4, -24.2,
-23.9, -23.6, -23.4, -23.1, -22.8, -22.5, -22.3, -22, -21.9,
-36.6, -36.6, -36.5, -36.3, -36.2, -35.7, -35.1, -34.5, -34.1,
-34.5, -34.7, -34, -33.3, -33, -32.6, -32.6, -32.8, -33, -33.3,
-33.2, -32.9, -32.9, -33, -32.9, -33, -33.1, -33, -33, -32.9,
-32.8, -32.6, -32.4, -32.2, -32, -31.7, -31.4, -31.2, -31, -30.7,
-30.4, -30.1, -29.8, -29.4, -29.1, -28.8, -28.5, -28.3, -28,
-27.8, -27.5, -27.2, -26.9, -26.7, -26.4, -26.1, -25.9, -25.6,
-25.4, -25.2, -24.9, -24.7, -24.4, -24.2, -23.9, -23.6, -23.4,
-23.1, -22.9, -22.6, -22.3, -22, -21.9, -36.4, -36.3, -36, -35.7,
-35.3, -34.5, -34.3, -34.5, -34.5, -34.3, -33.9, -33.5, -33.2,
-32.9)), row.names = c(NA, 300L), class = "data.frame")
I wish to subset t2m.all to isolate a single row returning the temperature value for a specified latitude and longitude pairing. This works for some coordinates but strangely not for others. For example, the below code works for these coordinates:
res = subset(t2m.all, Lon == -100.7 & Lat == 59.8)
but does not work for these coordinates:
res = subset(t2m.all, Lon == -100.7 & Lat == 59.6)
Both set of coordinates are clearly in the t2m.all dataframe, so why can R subset for some sets of coordinates but not for others? The logic seems sound to me, so I'm not sure what can be wrong. I have spent a long time going through all possibilities and troubleshooting on Stackoverflow, but I have yet to find a solution. Any suggestions? Many thanks in advance.
The issue results from round-off errors. You could set a tolerance value when comparing Lon & Lat with a certain value. In base you could use abs(x-y) < 1e-5 to achieve it:
t2m.all |>
subset(abs(Lon - -100.7) < 1e-5 & abs(Lat - 59.6) < 1e-5)
# Lon Lat X1958.01.01.00.00.00
# 1 -100.7 59.6 -32.9
The dplyr equivalent is near():
library(dplyr)
t2m.all %>%
filter(near(Lon, -100.7) & near(Lat, 59.6))
# Lon Lat X1958.01.01.00.00.00
# 1 -100.7 59.6 -32.9
Related
I have been using dplyr::summarise to sum daily data by month and have just spent a few hours finally figuring out that for whatever reason the decimal places in my raw data was throwing the function off.
Basically, the monthly sums for 'temp1' (which is the 'Mod' data) are wrong (the digits look right but the decimal place is in the wrong place) until I apply a rounding function (rounded to 7 or less digits fixes the problem it seems, 8 just reproduces the problem)
temp1 <- as.data.frame(read.csv("data/ModObs.csv"))
temp1$Date <- as.Date(temp1$Date, "%d/%m/%Y")
((((temp1$Obs <- round(temp1$Obs, 7))))#this line fixes the problem
((((temp1$Mod <- round(temp1$Mod, 7))))#this line fixes the problem
temp1$yearmonth <- lubridate::floor_date(as.Date(temp1$Date), 'month')
temp2 <- dplyr::group_by(temp1, yearmonth)
temp2 <- dplyr::summarise(temp2, Obs = sum(Obs, na.rm=TRUE))
temp1 <- dplyr::group_by(temp1, yearmonth)
temp1 <- dplyr::summarise(temp1, Mod = sum(Mod, na.rm=TRUE))
My question is, is there a way of using dplyr::summarise that means I would not have encountered this issue (which took me quite a while to figure out and solve)?
dput with error:
dput(temp1)
structure(list(yearmonth = structure(c(0, 31, 59, 90, 120, 151,
181, 212, 243, 273, 304, 334, 365, 396, 424, 455, 485, 516, 546,
577, 608, 638, 669, 699, 730, 761, 790, 821, 851, 882, 912, 943,
974, 1004, 1035, 1065, 1096, 1127, 1155, 1186, 1216, 1247, 1277,
1308, 1339, 1369, 1400, 1430, 1461, 1492, 1520, 1551, 1581, 1612,
1642, 1673, 1704, 1734, 1765, 1795, 1826, 1857, 1885, 1916, 1946,
1977, 2007, 2038, 2069, 2099, 2130, 2160, 2191, 2222, 2251, 2282,
2312, 2343, 2373, 2404, 2435, 2465, 2496, 2526, 2557, 2588, 2616,
2647, 2677, 2708, 2738, 2769, 2800, 2830, 2861, 2891, 2922, 2953,
2981, 3012, 3042, 3073, 3103, 3134, 3165, 3195, 3226, 3256, 3287,
3318, 3346, 3377, 3407, 3438, 3468, 3499, 3530, 3560, 3591, 3621,
3652, 3683, 3712, 3743, 3773, 3804, 3834, 3865, 3896, 3926, 3957,
3987, 4018, 4049, 4077, 4108, 4138, 4169, 4199, 4230, 4261, 4291,
4322, 4352, 4383, 4414, 4442, 4473, 4503, 4534, 4564, 4595, 4626,
4656, 4687, 4717, 4748, 4779, 4807, 4838, 4868, 4899, 4929, 4960,
4991, 5021, 5052, 5082, 5113, 5144, 5173, 5204, 5234, 5265, 5295,
5326, 5357, 5387, 5418, 5448, 5479, 5510, 5538, 5569, 5599, 5630,
5660, 5691, 5722, 5752, 5783, 5813, 5844, 5875, 5903, 5934, 5964,
5995, 6025, 6056, 6087, 6117, 6148, 6178, 6209, 6240, 6268, 6299,
6329, 6360, 6390, 6421, 6452, 6482, 6513, 6543, 6574, 6605, 6634,
6665, 6695, 6726, 6756, 6787, 6818, 6848, 6879, 6909, 6940, 6971,
6999, 7030, 7060, 7091, 7121, 7152, 7183, 7213, 7244, 7274), class = "Date"),
Obs = c(29.5, 1.6, 225.9, 305, 485.9, 392.6, 86.7, 422.1,
262.6, 22.6, 12.7, 40.3, 96.5, 1.4, 0, 40.1, 251.9, 494.4,
181.6, 86, 69.6, 128.4, 560.9, 55.8, 14.2, 94.1, 8.9, 28.2,
10.3, 1.4, 2.6, 9, 14.2, 5.6, 9.5, 1.6, 0, 195.1, 24.3, 18.9,
26, 34.3, 68.1, 144.1, 213.3, 99.2, 36.1, 17, 19.5, 13.3,
0, 352.7, 2812.7, 82, 311.9, 314.2, 397.5, 783.7, 388.7,
40.4, 1.3, 0, 2.1, 2.9, 2.1, 8.6, 1.2, 260.8, 494.4, 1125.1,
983.3, 20.9, 3.2, 0, 0.1, 1.1, 0.6, 32.9, 16.2, 124.1, 204.3,
340.6, 212.6, 42.8, 56.7, 2.2, 22.6, 78.3, 100.2, 786.4,
990.7, 330.2, 119.9, 2.7, 3.5, 5.7, 9.4, 17.6, 104.2, 168.9,
43, 118.4, 69.7, 479.3, 435.4, 101.4, 52.9, 104, 28.6, 0.8,
1.8, 13.1, 54.4, 26.2, 6.3, 24.8, 30.7, 53.9, 1.4, 0, 4.6,
0, 1.5, 14.5, 67.3, 217.4, 460.5, 251.2, 72.3, 29.3, 57.7,
29.7, 1.4, 1, 30.4, 3.6, 164.2, 378.4, 861.1, 982.5, 116.5,
68.4, 33.9, 1.5, 52.3, 7.5, 18.2, 70.4, 75.9, 158.9, 26.1,
2.3, 6, 8.4, 0.1, 43.9, 8.3, 0, 0.7, 8.6, 38.3, 24.2, 110.1,
164.1, 239, 120.8, 23.9, 24.7, 1.8, 1.4, 54.7, 75.6, 11.6,
19.6, 69.4, 199.7, 648.3, 260.9, 53.5, 4.5, 0.5, 0, 0.6,
60.9, 9, 130.9, 61.7, 539.5, 222.1, 31.6, 19.8, 288.6, 83.3,
2, 1.8, 104.4, 214, 108.3, 504.2, 152.6, 110.2, 103.3, 14.7,
128.2, 3.5, 2.5, 7.2, 47.4, 73.6, 116.2, 150.6, 161.1, 58.8,
32.9, 12.1, 33.3, 31.3, 0.7, 39.7, 0.3, 26, 102.2, 55.2,
46.3, 62.3, 15.4, 200.2, 98.9, 35.5, 0.4, 80.3, 286.5, 348.8,
646.5, 340.3, 1048.5, 558.6, 365.5, 129.4, 3.7), Mod = c(58.456732574,
0.647399496, 106.77816386, 267.838017351, 599.939323463,
250.80934844, 113.281660213, 241.663996002, 127.530387061,
52.687410089, 84.890244021, 41.364802773, 59.23208781, 8.497558874,
0.672761812, 15.465132304, 358.926445816, 399.9093607, 97.971842098,
42.72450411, 78.475537521, 267.696647395, 1499.730009232,
164.134543701, 15.739950594, 117.176571603, 0.29960511, 33.153451885,
71.35707594, 1.976493212, 38.99406048, 58.699745671, 88.893788732,
55.590919209, 17.675911123, 0.323688533, 0.802922429, 255.339027286,
86.973361482, 56.672316286, 195.494804037, 113.402888496,
88.016557451, 146.313739207, 141.11162499, 309.49712486,
42.342303882, 32.801816137, 9.804984811, 14.876734504, 0.741273571,
432.148407136, 2516.875488309, 47.539316029, 269.405152962,
183.64372206, 154.563624943, 467.720012557, 153.054373772,
32.514885627, 1.830055421, 0.066762771, 1.044433442, 1.346976081,
17.458179607, 49.907434727, 53.305731876, 353.57856375, 310.529543548,
962.398015832, 344.181844335, 30.810939684, 8.040785393,
0.377896164, 0.798674902, 0.801987649, 0.691369382, 13.928109124,
12.703685263, 137.85141766, 211.000002457, 444.374773665,
187.06473363, 56.579158088, 79.307114494, 1.185915374, 7.450495202,
350.92445957, 168.333585374, 1103.415013415, 530.738230571,
97.400577403, 120.218466778, 26.53863178, 4.95759286, 1.415953207,
1.349259407, 7.598631896, 31.687964985, 111.63067543, 253.033200389,
260.084267318, 174.328538378, 435.075601539, 266.057507136,
169.491413576, 370.501536962, 325.734910145, 52.804905885,
0.637640491, 0.749393501, 61.044014158, 236.352010674, 69.236802018,
50.981912279, 113.755615714, 68.015519965, 228.376481539,
1.35494224, 0.370446501, 1.782754512, 0.200525121, 0.567715904,
2.562157517, 66.360280078, 537.409598471, 548.857756317,
131.818783821, 79.38907511, 216.219977069, 86.85853468, 91.042441797,
1.310072508, 4.6784498, 49.359916771, 12.139100379, 792.36086926,
360.543361637, 674.819587278, 417.14625705, 71.213853069,
88.470327459, 26.501678301, 0.660446628, 1.983262203, 0.053393889,
2.116900185, 103.974625465, 146.1406309, 241.456322328, 41.761031962,
19.165518836, 61.329157567, 77.461701504, 0.717057613, 60.244865985,
2.038796249, 0.024612503, 1.15999722, 0.987871135, 66.752973657,
101.602951298, 207.507552152, 351.874694806, 239.490966404,
373.402963887, 83.392418938, 96.713198206, 2.471314963, 1.789748376,
23.642411238, 274.437164678, 27.196302352, 122.879115856,
203.243972815, 261.450286079, 674.337097864, 201.592587766,
66.457305017, 14.265446489, 11.688820111, 0.287104024, 0.498545345,
10.595714786, 64.36811409, 147.944544256, 105.263660789,
348.781394762, 115.965911604, 81.556952547, 35.877763907,
293.156577573, 122.052605838, 0.891615203, 0.201455399, 17.693507458,
191.862026713, 93.030313466, 379.074639489, 88.590763754,
138.225716958, 438.407332197, 11.107850781, 175.835916749,
0.793580574, 0.755295219, 1.253581528, 2.175241521, 126.829190302,
167.624256025, 261.538659971, 143.633607733, 58.216055381,
101.857571372, 37.192461414, 112.344312062, 100.262190061,
2.378722279, 4.946631624, 0.435401092, 51.481605801, 155.384067186,
153.115869623, 128.316180053, 153.07003862, 80.585921934,
221.89445498, 62.047224666, 105.157734971, 0.203008456, 51.168132113,
310.567488885, 238.329914336, 783.724869869, 193.016695288,
431.815627948, 143.854730373, 358.082995503, 84.620059176,
1.146042245)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-240L))
Your data is exactly the same before and after the rounding.
library(dplyr)
temp1 <- as.data.frame(read.csv("C:/Users/xxx/Documents/modops.csv"))
temp1$Date <- as.Date(temp1$Date, "%d/%m/%Y")
temp1$yearmonth <- lubridate::floor_date(as.Date(temp1$Date), 'month')
notfixed <- dplyr::group_by(temp1, yearmonth) %>% summarise(Mod = sum(Mod, na.rm=TRUE))
temp1$Obs <- round(temp1$Obs, 7)#this line fixes the problem
fixed <- dplyr::group_by(temp1, yearmonth) %>% summarise(Mod = sum(Mod, na.rm=TRUE))
> identical(fixed, notfixed)
[1] TRUE
I am trying to find a specific value, in this case 0, multiple times in a time series. The data looks like this
structure(list(time = c(40, 41, 42, 43, 44, 44.9, 45.9, 46.9, 47.9, 48.9, 49.9, 50.8, 51.8, 52.8, 53.7, 54.6, 55.6, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.4, 77.3, 78.3, 79.3, 80.3, 81.2, 82.2, 83.2, 84.2, 85.2, 86.2, 87.2, 88.2, 89.2, 90.2, 91.2, 92.2, 93.2, 94.2, 95.2, 96.2, 97.2, 98.2, 99.2, 100.2, 101.2, 102, 103, 103.9, 104.9, 105.9, 106.8, 107.8, 108.8, 109.8, 110.8, 111.8, 112.8, 113.8, 114.4, 114.9, 115.8, 116.8, 117.8, 118.8, 119.8), value = c(33.6, 33.6, 33.6, 33.6, 33.6, 33.6, 34, 34, 34.4, 34.72, 29.12, 34.8, 19.04, 30.32, 1.36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.48, 28.64, 32, 32, 32, 32, 32, 32, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7.68, 31.12, 32, 32, 31.6, 31.6, 31.6, 31.6, 31.6, 31.6, 31.6, 31.6, 31.6, 32, 32, 32, 2.8, 0, 0, 0, 0, 0, 0, 0, 0, 22.16, 32, 31.92, 31.92, 38.8, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, -83L), class = "data.frame")
The problem is I want to find the times of the first zeros in value appearing for some time. So the process is like: If value goes down to zero give me the time for this point in time, then if value went up for time and then goes down again to zero give me the time for this point in time etc...
Plotting the data should help to understand the problem.
So the result should look like this for time: 54.6, 76.4, 102, 114.4
Edit: I don't know if it is important but the original data is in a data.table.
There's probably a more elegant way to do this. Here I took the cumulative sum of values equal to zero, then, evaluated the "run length." Maybe easiest to understand running the code piece by piece working inside-out. In a nutshell, I used rle and cumsum to solve.
o <- structure(list(time = c(40, 41, 42, 43, 44, 44.9, 45.9, 46.9, 47.9, 48.9, 49.9, 50.8, 51.8, 52.8, 53.7, 54.6, 55.6, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.4, 77.3, 78.3, 79.3, 80.3, 81.2, 82.2, 83.2, 84.2, 85.2, 86.2, 87.2, 88.2, 89.2, 90.2, 91.2, 92.2, 93.2, 94.2, 95.2, 96.2, 97.2, 98.2, 99.2, 100.2, 101.2, 102, 103, 103.9, 104.9, 105.9, 106.8, 107.8, 108.8, 109.8, 110.8, 111.8, 112.8, 113.8, 114.4, 114.9, 115.8, 116.8, 117.8, 118.8, 119.8), value = c(33.6, 33.6, 33.6, 33.6, 33.6, 33.6, 34, 34, 34.4, 34.72, 29.12, 34.8, 19.04, 30.32, 1.36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.48, 28.64, 32, 32, 32, 32, 32, 32, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7.68, 31.12, 32, 32, 31.6, 31.6, 31.6, 31.6, 31.6, 31.6, 31.6, 31.6, 31.6, 32, 32, 32, 2.8, 0, 0, 0, 0, 0, 0, 0, 0, 22.16, 32, 31.92, 31.92, 38.8, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, -83L), class = "data.frame")
tmp <- rle(cumsum(o$value == 0))$lengths
o[cumsum(tmp)[tmp > 1] + 1,"time"]
[1] 54.6 76.4 102.0 114.4
An option with tidyverse and rleid from data.table
library(data.table)
library(dplyr)
o %>%
group_by(grp = rleid(value == 0)) %>%
filter(value == 0) %>%
group_by(grp) %>%
slice(1) %>%
ungroup %>%
select(time)
# A tibble: 4 x 1
# time
# <dbl>
#1 54.6
#2 76.4
#3 102
#4 114.
I have a data frame with 4000 columns and daily observations sorted by time. I want to create new columns that lag all existing columns 50 times in the past. So for a column Y create 50 additional columns that are Y-1day,Y-2days,Y-3days...Y-50days.
So far I've wrapped the following loop which does what I need to make.
The issue is that it's not very fast. Is there a more efficient way I can test?
for(i in 2:ncol(Data)){
for(j in 1:50){
Data<- slide(Data, Var = names(Data[i]), slideBy = -j)
}}
I'm attaching a snapshot of my data frame for reproducible example:
structure(list(time = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,
77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,
93, 94, 95, 96, 97, 98, 99, 100), A = c(17.081545, 16.630901,
16.623749, 16.258942, 16.244635, 16.165951, 15.886981, 15.865522,
15.529327, 15.772532, 16.04435, 15.779685, 15.915594, 15.593705,
15.336195, 15.593705, 15.736767, 15.736767, 15.457797, 15.815451,
16.108727, 16.237482, 15.808297, 16.058655, 16.53791, 16.988556,
16.516453, 16.480686, 16.967096, 17.181688, 17.446352, 17.11731,
16.952789, 16.8598, 16.795422, 16.437769, 16.587982, 16.845493,
17.167381, 17.510729, 17.410587, 17.474964, 17.246065, 17.703863,
17.424892, 17.174536, 17.103004, 16.695278, 16.93133, 16.638054,
16.115879, 16.20887, 15.987124, 16.151646, 16.151646, 16.115879,
16.173105, 16.101574, 16.080114, 15.9299, 15.879828, 15.786839,
15.314735, 15.27897, 15.493563, 15.436337, 15.286123, 15.121602,
15.27897, 14.88555, 14.785408, 14.592275, 14.785408, 14.856938,
14.670959, 15.243204, 15.09299, 15.250358, 15.264664, 15.18598,
14.771102, 14.842632, 15, 15.150214, 15.200286, 15.078684, 15.379113,
15.658083, 15.636623, 15.879828, 15.715307, 15.729613, 15.422031,
16.080114, 16.39485, 16.502146, 16.74535, 16.902718, 17.088697,
16.831188), AAP = c(29.033333, 28.84, 28.893333, 28.866667, 28.700001,
28.799999, 28.973333, 28.866667, 28.806667, 28.973333, 29.713333,
29.033333, 28.626667, 28.546667, 28.173334, 28.166666, 28.24,
28.553333, 28.366667, 28.733334, 28.833334, 28.9, 29.166666,
29.846666, 30.08, 30.093334, 29.673334, 29.860001, 30.053333,
30.186666, 29.833334, 29.673334, 34.533333, 33.82, 33.373333,
33.633335, 33.593334, 33.833332, 33.586666, 33.946667, 34.66,
34.599998, 34.84, 34.779999, 34.093334, 33.713333, 33.560001,
33.933334, 33.086666, 33.139999, 33.279999, 33.200001, 33.259998,
32.466667, 32.713333, 32.686668, 33.053333, 33.806667, 33.333332,
33.613335, 33.633335, 33.799999, 34.206665, 34.5, 34.166668,
34.206665, 33.933334, 34, 34.373333, 33.700001, 33.173332, 32.633335,
32.639999, 34.013332, 33.566666, 34.053333, 34.053333, 34.826668,
35.106667, 35.68, 35.653332, 35.566666, 35.380001, 35.419998,
35.966667, 36.573334, 36.673332, 36.486668, 36.286667, 36.099998,
35.433334, 35.419998, 35.84, 36.533333, 36.779999, 38.98, 39.633335,
39.646667, 39.486668, 39.433334), AAPL = c(4.520714, 4.567143,
4.607143, 4.610714, 4.946429, 4.925714, 4.611429, 4.675714, 4.985714,
5.014286, 5.046429, 4.991428, 5.032857, 5.035, 5.054286, 5.146429,
5.160714, 5.188571, 5.284286, 5.492857, 5.537857, 5.687857, 5.557857,
5.631429, 5.638571, 5.778572, 5.624286, 5.597143, 5.800714, 6.045,
6.315, 6.437857, 6.272143, 6.200714, 6.092143, 6.302143, 6.352143,
6.356429, 6.408571, 6.357143, 6.302857, 5.97, 6.115714, 6.107143,
5.79, 5.621428, 5.69, 5.752857, 5.76, 5.851429, 5.882857, 6.035714,
6.137143, 6.242857, 6.118571, 6.078571, 6.071429, 6.075714, 5.964286,
6.114286, 5.952857, 5.841429, 5.87, 5.984286, 6.047143, 6.222857,
6.248571, 5.988572, 6.094285, 5.862857, 5.322857, 5.05, 5.088572,
5.298572, 5.072857, 5.311429, 5.071429, 5.282857, 5.17, 5.135714,
5.077143, 5.151429, 5.204286, 5.172857, 5.307143, 5.24, 5.32,
5.281428, 5.202857, 5.087143, 4.875714, 4.967143, 5.078571, 5.051429,
5.12, 5.364286, 5.364286, 5.68, 5.671429, 5.682857), ABC = c(14.5375,
14.4225, 14.395, 14.5175, 14.475, 14.475, 14.51, 14.515, 14.275,
14.3175, 14.4875, 14.375, 14.5025, 14.2525, 14.3925, 14.13, 14.47,
14.365, 14.5925, 14.57, 14.74, 14.71, 14.995, 14.9, 14.8625,
15.0325, 14.78, 14.875, 15.085, 15.0525, 15.4275, 15.3075, 14.9225,
15, 14.7025, 14.7975, 15, 15, 14.975, 15.3775, 15.435, 15.5325,
15.6625, 15.6575, 15.695, 15.1275, 15.1025, 15.0775, 15.265,
15.0325, 14.905, 15.1975, 15.215, 15.2025, 15.1025, 15.3775,
15.2775, 13.5075, 13.5275, 13.95, 14.3225, 14.09, 14.4275, 14.735,
14.6475, 14.8, 14.4575, 14.62, 14.7525, 14.7, 14.9, 15.125, 14.83,
14.9525, 14.825, 14.9625, 15, 14.975, 14.9675, 15.0975, 15.0875,
15.32, 15.5125, 15.38, 15.51, 15.575, 15.7475, 15.9975, 15.9175,
15.895, 15.955, 15.98, 16.209999, 16.459999, 16.5725, 16.514999,
16.4925, 16.5, 16.495001, 16.4825), ABMD = c(15.01, 14.98, 14.69,
14.52, 14.29, 14.42, 14.31, 14.17, 12.45, 12.05, 11.87, 11.97,
11.41, 11.16, 11.06, 11.2, 11.1, 11.57, 11.43, 11.88, 11.58,
11.12, 11.16, 11.32, 10.97, 10.88, 10.72, 10.3, 10.75, 10.25,
10.29, 10.41, 10.02, 10.05, 10.08, 10, 10.24, 10.89, 10.7, 10.8,
10.66, 10.71, 11.12, 11.18, 11.2, 10.95, 11.07, 11.12, 11.3,
11.19, 10.83, 10.56, 10.37, 10.47, 10.33, 10.17, 10.51, 10.4,
10.56, 10.74, 10.58, 10.6, 10.57, 10.71, 11.23, 11.28, 11.51,
11.15, 10.98, 10.98, 11.05, 10.76, 10.96, 11.1, 10.62, 11.1,
10.53, 10.69, 10.65, 10.73, 10.15, 10.15, 9.52, 9.6, 9.6, 9.52,
9.47, 9.44, 9.35, 9.27, 9.13, 8.92, 9.26, 9.45, 9.97, 10.25,
10.28, 9.99, 10.16, 10.17), ABT = c(22.392265, 22.166759, 21.912466,
22.40666, 22.790501, 23.011208, 22.588984, 22.517014, 22.085194,
22.19075, 22.089993, 22.09479, 21.95085, 22.061205, 22.037214,
22.027618, 22.018023, 21.811708, 21.720547, 21.600595, 21.854891,
21.898071, 21.907667, 21.840496, 21.874083, 21.725344, 21.667768,
21.581404, 22.166759, 22.305902, 22.488226, 22.469034, 22.339487,
22.26272, 21.802113, 21.946053, 22.243528, 22.200346, 22.066002,
22.051607, 22.099588, 22.075598, 22.267517, 22.382669, 22.310699,
22.02282, 22.209942, 22.070801, 22.128376, 21.907667, 21.792517,
21.365494, 21.336706, 21.048826, 20.996048, 21.39908, 21.562212,
21.677364, 21.95085, 22.430651, 22.368277, 22.161963, 22.157164,
22.646561, 22.843279, 23.19833, 22.963228, 22.91045, 22.98242,
23.049591, 23.169542, 23.927626, 23.500605, 23.111965, 22.69454,
23.078381, 22.824085, 22.920046, 23.001612, 23.255905, 23.073582,
23.586967, 23.692524, 23.634949, 23.850859, 23.601362, 23.519796,
23.543785, 23.438231, 23.634949, 23.567776, 23.395048, 23.735706,
23.706919, 23.678129, 23.529392, 23.452623, 23.366261, 23.351866,
23.145552), ACN = c(26.370001, 25.75, 25.65, 25.42, 26.610001,
26.959999, 26.5, 26.389999, 26.18, 26.290001, 26.1, 26, 25.67,
25.16, 24.9, 25.200001, 25.4, 25.68, 25.6, 26.049999, 25.99,
25.83, 25.48, 25.73, 25.77, 25.85, 25.51, 25.42, 25.200001, 24.639999,
24.9, 25.049999, 24.51, 24.9, 24.799999, 24.709999, 24.48, 25.15,
25.549999, 25.59, 25.42, 25.110001, 25.370001, 25.49, 25.32,
25.17, 24.950001, 24.459999, 24.48, 23.98, 24.030001, 23.950001,
23.66, 24.01, 24.280001, 24.299999, 24.4, 24.57, 24.16, 24.559999,
24.15, 24.440001, 24.35, 24.860001, 24.969999, 24.889999, 23.700001,
23.34, 23.440001, 23.120001, 22.860001, 22.5, 22.57, 22.440001,
21.9, 21.959999, 21.75, 21.85, 21.549999, 21.469999, 21.620001,
21.700001, 21.969999, 22.1, 22.1, 21.82, 22, 22.08, 21.860001,
21.92, 21.99, 22.049999, 22.01, 22.049999, 22.5, 22.790001, 22.719999,
22.76, 22.67, 22.34), ADBE = c(30.844999, 30.030001, 29.865,
29.370001, 29.389999, 29.41, 29.059999, 29.49, 29.110001, 29.115,
29.190001, 28.940001, 29.035, 28.535, 27.695, 27.790001, 28.004999,
28.084999, 27.74, 28.450001, 28.950001, 31.145, 31.709999, 31.995001,
31.76, 31.85, 31.295, 31.34, 31.85, 31.735001, 32.455002, 32.299999,
31.535, 31.415001, 30.754999, 30.875, 30.695, 30.715, 30.875,
31.17, 31.174999, 31.174999, 31.885, 32.535, 32.474998, 32.255001,
32.654999, 32.209999, 32.669998, 32.27, 31.594999, 31.945, 33.904999,
33.349998, 33.18, 33.134998, 33.27, 33.555, 33.110001, 33.865002,
33.584999, 33.380001, 33.290001, 33.424999, 34.049999, 34.195,
33.630001, 33.400002, 33.450001, 32.535, 31.74, 30.33, 27.385,
29.049999, 28.625, 29.77, 30.145, 30.02, 29.559999, 29.225, 29.235001,
29.735001, 28.575001, 28.645, 28.775, 28.459999, 28.85, 29.334999,
28.76, 28.965, 28.889999, 29.049999, 29.955, 29.889999, 30.549999,
31.059999, 31.115, 31.360001, 32.419998, 32.759998), ADI = c(36.389999,
35.400002, 35.560001, 35.5, 35.549999, 35.41, 35.080002, 35.560001,
35.099998, 35.639999, 36.07, 35.139999, 34.650002, 34.470001,
34.049999, 34.299999, 34.880001, 34.830002, 34.740002, 35.889999,
35.990002, 36.009998, 35.240002, 37.52, 37.52, 38.02, 37.18,
36.830002, 38.049999, 37.599998, 37.32, 37.130001, 36.700001,
36.299999, 36.5, 36.59, 37.32, 37.5, 36.720001, 38, 37.709999,
36.93, 37.119999, 37.049999, 36.950001, 36.919998, 37.849998,
37.130001, 37.209999, 36.57, 35.919998, 36.02, 35.830002, 35.709999,
35.830002, 36.23, 35.799999, 35.66, 35.119999, 36.330002, 36.139999,
35.709999, 35.599998, 35.310001, 35.41, 36.09, 35.669998, 35.34,
34.93, 34.099998, 33.650002, 32.84, 33.360001, 33.849998, 33.419998,
34.349998, 33.799999, 33.700001, 33.52, 33.360001, 33.52, 34.110001,
33.849998, 33.669998, 34.560001, 34.619999, 34.619999, 34.549999,
34.130001, 34.060001, 34.310001, 35.490002, 36.419998, 36.700001,
36.860001, 36.889999, 37.080002, 36.529999, 36.849998, 36.290001
)), row.names = c(NA, 100L), class = "data.frame")
We can use shift from data.table which can take a vector of values for n
library(data.table)
setDT(Data)
out <- Data[, shift(.SD, n = 1:50), .SDcols = -1]
names(out) <- paste0(rep(names(Data)[-1], each = 50), "_", 1:50, "days")
Data[, names(out) := out][]
I am trying to perform 6 months forecasting over production data for three power plants, I built my data as an hts object that has 3 levels. However, when I am performing the forecast function and then try to see the accuracy using test data I get the following error: "Error in x - fcasts: non-conformable arrays"
Furthermore, when I try to apply the "arima" as a forecasting method on the hts object I get the following (the warning message is repeated 9 times, as I have 9 time series in the hts object):
forecasts <- forecast(data,h = 6 , method = "bu" , fmethod = "arima")
I used the following instructions to get the hts object:
and the data has the following structure:
I am not sure where I am going wrong. Anyone can help with some thoughts??
Thank you!
The data:
structure(list(LarGroup1 = c(188.3, 187.2, 94.7, 109.2, 202.7,
146.6, 121.9, 151.3, 111.1, 103.4, 188.1, 168.1, 233.9, 230.7,
187.1, 0, 98.9, 173.5, 149.4, 168.6, 4.7, 14.8, 91.8, 166.5,
170.5, 123.6, 85.2, 64.4), LarGroup2 = c(159.1, 127.7, 210.3,
199.8, 113, 143.4, 144.5, 83.8, 41.6, 35.1, 95.2, 178.2, 241.1,
236.4, 181.9, 194.3, 196.1, 92.4, 154.6, 78.9, 35.7, 0, 74.5,
75.1, 140, 142.5, 3.8, 17.5), RibGroup1 = c(49.4, 102.4, 50.8,
118.8, 108.4, 139.5, 121.7, 69.6, 53.4, 28, 113.3, 96.3, 70.8,
124.4, 54.4, 128.7, 63.3, 2.1, 41.3, 0.4, 0.6, 0, 5.4, 57.9,
9.9, 30, 221, 167.2), RibGroup2 = c(32.7, 32, 98.1, 6.3, 85.5,
96.6, 41.1, 44.9, 50.4, 27.3, 0, 45.4, 199.1, 179.2, 86.1, 0,
58.4, 43.3, 41.8, 42.1, 22.1, 11.8, 71.8, 112, 204.1, 40.9, 24.5,
210.9), RibGroup3 = c(90.8, 15.4, 10.5, 124.4, 33.9, 8.4, 38.3,
56.9, 13.5, 0, 32.6, 132.8, 160.7, 168.7, 60.7, 131.9, 110.8,
29.2, 131.3, 62.1, 6.1, 0, 0, 3.4, 23.9, 192.7, 165.5, 0), SinGroup1 = c(235.2,
225.4, 226.1, 234.4, 222.1, 232.3, 233.4, 201.9, 195.3, 209.4,
233.6, 223.6, 222.2, 232, 224, 149.8, 201.6, 220.2, 203.1, 212.1,
71.9, 82.3, 183.2, 210.6, 198.6, 230.8, 218, 163.2), SinGroup2 = c(233.4,
225.6, 227, 51.6, 76, 230.7, 233.1, 202.7, 200.2, 207.2, 228.4,
226.2, 183.9, 230.4, 222.3, 227.7, 177.9, 152, 218.6, 210.6,
80.9, 63.2, 188.1, 209.5, 233.2, 210.1, 226.5, 200.5), SinGroup3 = c(233.2,
188.5, 226.9, 234.7, 222.8, 234.6, 220.6, 156.4, 209.2, 218.7,
232.9, 226.1, 215.4, 231, 222.7, 222.7, 183.7, 203.8, 216.8,
112, 0, 39.6, 180.8, 203.6, 221.1, 228.9, 202.8, 186.7), SinGroup4 = c(218,
215.5, 226.8, 235.6, 223.6, 234.8, 234.9, 69.3, 192, 207.8, 235.2,
217.2, 235.1, 231.8, 223.5, 230.5, 225.6, 220.1, 220, 211.9,
114.8, 44.5, 158.5, 206.3, 231.8, 179, 225.3, 198.6)), class = "data.frame", row.names = c(NA,
-28L))
In the accuracy function, you need to include test data, not training data. You ask for 6 steps ahead, but your test data only consists of 4 time periods.
The seasonal differencing error suggests you are using an old version of the forecast package. Please update your packages.
The following code works using current CRAN packages (forecast v8.4, hts v
library(hts)
Production_data <- data.frame(
LarGroup1 = c(
188.3, 187.2, 94.7, 109.2, 202.7,
146.6, 121.9, 151.3, 111.1, 103.4, 188.1, 168.1, 233.9, 230.7,
187.1, 0, 98.9, 173.5, 149.4, 168.6, 4.7, 14.8, 91.8, 166.5,
170.5, 123.6, 85.2, 64.4
), LarGroup2 = c(
159.1, 127.7, 210.3,
199.8, 113, 143.4, 144.5, 83.8, 41.6, 35.1, 95.2, 178.2, 241.1,
236.4, 181.9, 194.3, 196.1, 92.4, 154.6, 78.9, 35.7, 0, 74.5,
75.1, 140, 142.5, 3.8, 17.5
), RibGroup1 = c(
49.4, 102.4, 50.8,
118.8, 108.4, 139.5, 121.7, 69.6, 53.4, 28, 113.3, 96.3, 70.8,
124.4, 54.4, 128.7, 63.3, 2.1, 41.3, 0.4, 0.6, 0, 5.4, 57.9,
9.9, 30, 221, 167.2
), RibGroup2 = c(
32.7, 32, 98.1, 6.3, 85.5,
96.6, 41.1, 44.9, 50.4, 27.3, 0, 45.4, 199.1, 179.2, 86.1, 0,
58.4, 43.3, 41.8, 42.1, 22.1, 11.8, 71.8, 112, 204.1, 40.9, 24.5,
210.9
), RibGroup3 = c(
90.8, 15.4, 10.5, 124.4, 33.9, 8.4, 38.3,
56.9, 13.5, 0, 32.6, 132.8, 160.7, 168.7, 60.7, 131.9, 110.8,
29.2, 131.3, 62.1, 6.1, 0, 0, 3.4, 23.9, 192.7, 165.5, 0
), SinGroup1 = c(
235.2,
225.4, 226.1, 234.4, 222.1, 232.3, 233.4, 201.9, 195.3, 209.4,
233.6, 223.6, 222.2, 232, 224, 149.8, 201.6, 220.2, 203.1, 212.1,
71.9, 82.3, 183.2, 210.6, 198.6, 230.8, 218, 163.2
), SinGroup2 = c(
233.4,
225.6, 227, 51.6, 76, 230.7, 233.1, 202.7, 200.2, 207.2, 228.4,
226.2, 183.9, 230.4, 222.3, 227.7, 177.9, 152, 218.6, 210.6,
80.9, 63.2, 188.1, 209.5, 233.2, 210.1, 226.5, 200.5
), SinGroup3 = c(
233.2,
188.5, 226.9, 234.7, 222.8, 234.6, 220.6, 156.4, 209.2, 218.7,
232.9, 226.1, 215.4, 231, 222.7, 222.7, 183.7, 203.8, 216.8,
112, 0, 39.6, 180.8, 203.6, 221.1, 228.9, 202.8, 186.7
), SinGroup4 = c(
218,
215.5, 226.8, 235.6, 223.6, 234.8, 234.9, 69.3, 192, 207.8, 235.2,
217.2, 235.1, 231.8, 223.5, 230.5, 225.6, 220.1, 220, 211.9,
114.8, 44.5, 158.5, 206.3, 231.8, 179, 225.3, 198.6
)
)
Production_data_ts <- ts(Production_data, frequency = 12, start = c(2016, 7))
Production_data_hts <- hts(Production_data_ts, characters = c(3, 6))
data <- window(Production_data_hts, start = c(2016, 7), end = c(2018, 6))
test <- window(Production_data_hts, start = c(2018, 7), end = c(2018, 10))
forecasts <- forecast(data, h = 4, method = "bu")
accuracy(forecasts, test)
I am trying to plot a table in R and I am trying to format it so that is visually attractive for presentations.
I am trying to make it look like:
2000-01-01 2000-03-01 2000-06-01 ...
Revenue 3.5 4.6 7.9
Cost 2.3 2.7 5.6
And have the boxes that encapsulate the words, numbers, be the right size such that the column header dates and row labels are not squished . How do I do that??
plot.table(t(z))
z <- structure(c(68.2, 66.1, 64.7, 31.8, 30.9, 25.4, 36.1, 38.3, 38.3,
42.2, 43.3, 40.2, 41.9, 47.7, 50.8, 46.7, 48.2, 55.2, 58.2, 55.3,
58.2, 62.5, 62.2, 59.5, 59.3, 59.4, 58.7, 68.2, 64.9, 94.7, 75.7,
72, 73.5, 77.9, 83.8, 82.6, 83.8, 88.8, 91.5, 91.8, 92.6, 103.4,
100.5, 110.8, 105.4, 113.5, 110, 110.2, 118.9, 125.5, 122.5,
121.4, 122.6, 122.6, 127.4, 133.8, 131.5, 137.6, 142.7, 133,
39.8, 46.3, 38.2, 16.6, 14.5, 17.4, 17.7, 19.1, 19, 21.2, 20.9,
21.2, 19.9, 23.5, 25.2, 25.3, 23.3, 27.9, 29.3, 28.1, 29.6, 32.4,
31.3, 31.1, 31.3, 31.3, 31.5, 36, 36.9, 40.1, 39, 37.4, 38.1,
41.1, 43.1, 42.3, 42.4, 45.3, 46.4, 47.3, 48.2, 54.1, 51.6, 57.8,
54.3, 59.7, 56.1, 56.1, 60.9, 65.8, 62.8, 62.8, 62.1, 63.8, 65.5,
68.2, 66.7, 72.1, 75.1, 71.6), .Dim = c(60L, 2L), .Dimnames = list(
c("11323", "11413", "11504", "11596", "11688", "11778", "11869",
"11961", "12053", "12143", "12234", "12326", "12418", "12509",
"12600", "12692", "12784", "12874", "12965", "13057", "13149",
"13239", "13330", "13422", "13514", "13604", "13695", "13787",
"13879", "13970", "14061", "14153", "14245", "14335", "14426",
"14518", "14610", "14700", "14791", "14883", "14975", "15065",
"15156", "15248", "15340", "15431", "15522", "15614", "15706",
"15796", "15887", "15979", "16071", "16161", "16252", "16344",
"16436", "16526", "16617", "16709"), c("revenue", "cost")))